Abstract
Abstract. Marine gas seeps, such as in the Panarea area near Sicily (McGinnis et al., 2011), emit large amounts of methane and carbon-dioxide, greenhouse gases. Better understanding their impact on the climate and the marine environment requires precise measurements of the gas flux. Camera based bubble measurement systems suffer from defocus blur caused by a combination of small depth of field, insufficient lighting and from motion blur due to rapid bubble movement. These adverse conditions are typical for open sea underwater bubble images. As a consequence so called ’bubble boxes’ have been built, which use elaborate setups, specialized cameras and high power illumination. A typical value of light power used is 1000W (Leifer et al., 2003). In this paper we propose the compensation of defocus and motion blur in underwater images by using blind deconvolution techniques. The quality of the images can be greatly improved, which will relax requirements on bubble boxes, reduce their energy consumption and widen their usability.
Highlights
In areas like the Panarea area near Sicily (McGinnis et al, 2011) or the Tommelitten (Schneider von Deimling et al, 2011) in the North Sea, large amounts of carbon dioxide and methane are emitted from underwater gas seeps
The two main sources of blur identified in a bubble box are defocus blur and motion blur
First we will concentrate on defocus blur, which occurs if the bubbles are outside of the depth of field, centered at the focal plane
Summary
In areas like the Panarea area near Sicily (McGinnis et al, 2011) or the Tommelitten (Schneider von Deimling et al, 2011) in the North Sea, large amounts of carbon dioxide and methane are emitted from underwater gas seeps. Blind deconvolution techniques in general underwater settings have been used with success in (Fan et al, 2010a), (Fan et al, 2010b) and with the adapted Richardson-Lucy algorithm in (Wu et al, 2013), while the application on bubbly flow images is novel. We compare the heavy-tailed gradient sparsity MAP(Maximum a posteriori) blind deconvolution method by (Kotera et al, 2013) with the preceding standard technique and test whether they are suitable and applicable to underwater gas bubble images. Given the Bayesian theorem and the image model Equation 1, neglecting noise, the goal is finding the MAP(Maximum A Posteriori) of
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